The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Rainfall predictions have become an integral part of agricultural planning. Growers commonly make management decisions based on rainfall estimates. Rainfall estimations can be based on different types of rain sensing instruments including weather radars and rain gauges. Weather radars provide wide spatial coverage and average rainfall over a given area. However, radar based estimates may be biased because they depend on certain latent variables, such as rain drop size, and detect water content aloft as opposed to water surface content.
Rain gauges provide more accurate point estimates because they measure actual rain accumulation on the ground. However, rain-gauge data may vary based upon the location of the rain gauge in a field and may be localized to the fields where they are installed. Utilization of rain gauge instruments does not provide large spatial coverage to estimate large areas.
Additionally, precipitation estimates often include confidence bounds that are used to determine reliability of the precipitation estimates. However, confidence bounds are generally based upon static data such as historical precipitation observations. Historical precipitation observations do not account for the variability in the relationship between radar observations and rain gauge observations and do not account for different precipitation types that may occur or have occurred in the historical precipitation observations. Confidence bounds that are solely based on historical precipitation observations may lead to inaccurate reporting of the reliability of precipitation estimates to growers.